adding-dbt-unit-test

dbt unit tests are written as YAML definitions that mock upstream model inputs and assert expected outputs. You define the model name, given inputs, and expect sections within your schema.yml to validate logic during development.

dbt unit test fixtures support dict, csv, and sql formats for mocking input data. You can provide fixture-based data inline in the YAML or reference external files to cover complex input scenarios.

Yes, dbt unit testing handles advanced scenarios including model versioning, overrides, and ephemeral dependencies. The YAML schema supports versions and config keys to target specific model iterations correctly.

The standard dbt unit test YAML schema enforces keys such as model, given, expect, versions, overrides, and config. This structure ensures upstream inputs are mocked and expected outputs are validated consistently.

Yes, you can practice test-driven development in dbt by generating YAML unit test specs before building model logic. Define the given inputs and expected outputs first, then develop the model to pass the assertions.

Use dbt unit tests during model development to mock upstream inputs and validate complex transformation logic locally. They are ideal for TDD and edge-case validation, whereas generic data quality tests check existing table properties.